Summary of Private Language Models Via Truncated Laplacian Mechanism, by Tianhao Huang et al.
Private Language Models via Truncated Laplacian Mechanism
by Tianhao Huang, Tao Yang, Ivan Habernal, Lijie Hu, Di Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning models for Natural Language Processing (NLP) are susceptible to variants of privacy attacks. To mitigate privacy leakage, researchers have explored word-level perturbations based on formal guarantees from differential privacy (DP) in the embedding space. However, existing approaches often trade-off performance for high privacy or rely on weaker relaxations of DP that compromise privacy strength. This raises questions about designing a new method for private word embedding that overcomes these limitations. In this paper, we propose the High Dimensional Truncated Laplacian Mechanism (HD-TLM), a novel private embedding approach building upon the truncated Laplacian mechanism. Our HD-TLM introduces a non-trivial extension of the original one-dimensional space case. Theoretically, we demonstrate that our method has lower variance compared to previous private word embedding methods. To validate its effectiveness, we conduct comprehensive experiments on private embedding and downstream tasks using three datasets. Remarkably, even in high privacy regimes, our approach incurs only a slight decrease in utility compared to the non-private scenario. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping sensitive information safe in natural language processing models. When you share text data, it’s like sharing secrets – you don’t want them getting out! To keep those secrets safe, researchers have been working on ways to add noise to the words so that even if someone tries to sneak a peek, they won’t be able to understand what’s going on. The problem is that most of these methods are not very good at balancing safety and performance – they either sacrifice too much accuracy for safety or don’t keep secrets safe enough. This paper proposes a new way to do private word embedding that’s better than the existing approaches. They show that their method is more accurate even when keeping things super secretive, and it only loses a little bit of performance compared to not being secret at all. |
Keywords
* Artificial intelligence * Deep learning * Embedding * Embedding space * Natural language processing * Nlp
